A survey paper of virtual friend

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A Survey Paper of Virtual Friend Chatbot

Siddiq Abu Bakkar [09-13368-1] AMERICAN INTERNATIONAL UNIVERSITY BANGLADESH (AIUB)

CSE DEPARTMENT

[email protected] ; [email protected]

2012

Shaon [Type the company name]

3/20/2012

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Virtual Friend Chatbot Siddiq Abu Bakkar 09-13368-1

A Survey Paper of Virtual Friend Chatbot

Siddiq Abu Bakkar

09-13368-1 AMERICAN INTERNATIONAL UNIVERSITY BANGLADESH (AIUB)

CSE DEPARTMENT

[email protected] ; [email protected]

Abstract:

A chatter robot, chatterbot , chatbot or chat bot is a computer

program designed to simulate an intelligent conversation with one or more human users via auditory or textual

methods, primarily for engaging in small talk. The primary aim of such simulation

has been to fool the user into thinking that the program's output has been produced by a human (the Turing test). Programs

playing this role are sometimes referred to as Artificial Conversational Entities, talk

bots or chatterboxes. In addition, however, chatterbots are often integrated into dialog systems for various practical purposes

such as online help, personalized service, or information acquisition. Some

chatterbots use sophisticated natural language processing systems, but many simply scan for keywords within the input

and pull a reply with the most matching keywords, or the most similar wording

pattern, from a textual database.

Virtual Friend (VF) is a computer

program and early example of primitive natural language processing. VF operated

by processing user's response to scripts, the most famous of which was DOCTOR, a simulation of a Rogerian

psychotherapist. Eliza, using almost no information about human thought or

emotion, DOCTOR sometimes provided a startlingly human-like interaction .Eliza was written at MIT by Joseph

Weizaenbaum between 1964 and 1966.

When the ―USER‖ exceeded the

very small knowledge base, VF might provide a generic response, for example, responding to ―I won't go to university

today.‖ with ―Why you won't go to university, are you feeling sick?‖. The

response to ―Yahoo! I have got 3.94 CGPA in this semesters. ‖ would be ―Congratulation!! I am very much happy

for your excellent result.‖ VF is implemented using simple pattern

matching techniques, but is taken seriously by several of it users, even after explained to them how it worked.

Virtual Friend Response

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The program was designed to showcase the digitized voices the cards

were able to produce, though the quality was far from life-like. Its AI engine was likely based on something similar to

the ELIZA algorithm.

Contents:

1. Natural Language Processing

[NLP] 2. Machine Learning [ML]

I. Supervised learning

algorithms II. Logic based algo-

rithms

Decision

trees

III. Statistical learning

algorithms

Naive Bayes classifiers

Bayesian Networks

3. Speech Recognition [SR]

4. Turing Test [TT]

5. Most Popular Chatbots

a. ELIZA

b. PARRY

c. The Chinese Room

d. SIRI

i. Details of SIRI

ii. Reception Of SIRI

iii. SIRI says some weird things

6. References.

Natural Language Processing:

The history of machine translation

dates back to the seventeenth century, when philosophers such

as Leibniz and Descartes put forward proposals for codes which would relate words between languages. All of these

proposals remained theoretical, and none resulted in the development of an actual

machine.

The first patents for "translating

machines" were applied for in the mid-

1930s. One proposal, by Georges

Artsrouni was simply an automatic

bilingual dictionary using paper tape. The

other proposal, by Peter Troyanskii,

a Russian, was more detailed. It included

both the bilingual dictionary, and a method

for dealing with grammatical roles

between languages, based on Esperanto.

In 1950, Alan Turing published his

famous article "Computing Machinery and

Intelligence"[1] which proposed what is

now called the Turing test as a criterion of

intelligence. This criterion depends on the

ability of a computer program to

impersonate a human in a real-time written

conversation with a human judge,

sufficiently well that the judge is unable to

distinguish reliably - on the basis of the

conversational content alone - between the

program and a real human.

In 1957, Noam

Chomsky’s Syntactic

Structures revolutionized Linguistics with

'universal grammar', a rule based system of

syntactic structures. However, the real

progress of NLP was much slower, and

after the ALPAC report in 1966, which

found that ten years long research had

failed to fulfill the expectations, funding

was dramatically reduced internationally.

In 1969 Roger Schank introduced

the conceptual dependency theory for

natural language understanding. This

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model, partially influenced by the work

of Sydney Lamb, was extensively used by

Schank's students at Yale University, such

as Robert Wilensky, Wendy Lehnert,

andJanet Kolodner.

In 1970, William A. Woods

introduced the augmented transition

network (ATN) to represent natural

language input. Instead of phrase structure

rules ATNs used an equivalent set of finite

state automata that were called recursively.

ATNs and their more general format called

"generalized ATNs" continued to be used

for a number of years.

Machine Learning:

There are several applications for

Machine Learning (ML), the most signifi-cant of which is data mining. People are

often prone to making mistakes duringanalyses or, possibly, when trying to establish Relationships between multiple

features. This makes it difficult for them to find solutions to certain problems. Ma-

chine learning can often be successfully applied to these problems, improving the efficiency of systems and the designs of

machines. Every instance in any dataset used

by machine learning algorithms is repre-sented using the same set of features. The features may be continuous, categorical or

binary. If instances are given with known labels (the corresponding correct outputs)

then the learning is called supervised, in contrast to unsupervised learning, where instances are unlabeled. By applying these

unsupervised (clustering) algorithms, re-searchers hope to discover unknown, but

useful, classes of items (Jain et al., 1999). Another kind of machine learning

is reinforcement learning (Barto & Sutton,

1997). The training information provided to the learning system by the environment

(external trainer) is in the form of a scalar reinforcement signal that constitutes a measure of how well the system operates.

The learner is not told which actions to

take, but rather must discover which ac-tions yield the best reward, by trying each

action in turn.

Numerous ML applications involve tasks that can be set up as supervised. In the present paper, we have concentrated on

the techniques necessary to do this. In par-ticular, this work is concerned with classi-

fication problems in which the output of instances admits only discrete, unordered values. Instances with known labels (the

corresponding correct outputs) We have limited our references to recent

refereed journals, published books and conferences. In addition, we have added some references regarding the original

work that started the particular line of re-search under discussion. A brief review of

what ML includes can be found in (Dutton & Conroy, 1996). De Mantaras and Ar-mengol (1998) also presented a historical

survey of logic and instance based learning classifiers. The reader should be cautioned

that a single article cannot be a compre-hensive review of all classification learn-ing algorithms. Instead, our goal has been

to provide a representative sample of exist-ing lines of research in each learning tech-

nique. In each of our listed areas, there are many other papers that more comprehen-sively detail relevant work.

Supervised learning algorithms

Inductive machine learning is the

process of learning a set of rules from in-stances (examples in a training set), or more generally speaking, creating a classi-

fier that can be used to generalize from new instances. The process of applying

supervised ML to a real-world problem is described in Figure

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Figure: The process of supervised ML

The first step is collecting the da-

taset. If a requisite expert is available, then s/he could suggest which fields (attributes, features) are the most informative. If not,

then the simplest method is that of ―brute-force,‖ which means measuring everything

available in the hope that the right (in-formative, relevant) features can be isolat-ed. However, a dataset collected by the

―brute-force‖ method is not directly suita-ble for induction. It contains in most cases

noise and missing feature values, and therefore requires significant pre-processing (Zhang et al., 2002).

The second step is the data prepara-

tion and data preprocessing. Depending on the circumstances, researchers have a number of methods to choose from to han-

dle missing data (Batista & Monard, 2003). Hodge & Austin (2004) have re-

cently introduced a survey of contempo-rary techniques for outlier (noise) detec-tion. These researchers have identified the

techniques’ advantages and disadvantages. Instance selection is not

only used to handle noise but to cope with the infeasibility of learning from very

large datasets. Instance selection in these datasets is an optimization problem that

attempts to maintain the mining quality while minimizing the sample size (Liu and Motoda, 2001). It reduces data and enables

a data mining algorithm to function and work effectively with very large datasets.

There is a variety of procedures for sam-pling instances from a large dataset (Reinartz, 2002). Feature subset selection

is the process of identifying and removing as many irrelevant and redundant features

as possible (Yu & Liu, 2004). This reduces the dimensionality of the data and enables data mining algorithms to operate faster

and more effectively. The fact that many features depend on one another often

unduly influences the accuracy of super-vised ML classification models. This prob-lem can be addressed by constructing new

features from the basic feature set (Mar-kovitch & Rosenstein, 2002). This tech-

nique is called feature construc-tion/transformation. These newly generat-ed features may lead to the creation of

more concise and accurate classifiers. In addition, the discovery of meaningful fea-

tures contributes to better comprehensibil-ity of the produced class.

Logic based algorithms:

Decision trees: Murthy (1998) provided an over-

view of work indecision trees and a sample of their usefulness to newcomers as well as

practitioners in the field of machine learn-ing. Thus, in this work, apart from a brief description of decision trees, we will refer

to some more recent works than those in Murthy’s article as well as few very im-

portant articles that were published earlier. Decision trees are trees that classify in-stances by sorting them based on feature

values. Each node in a decision tree repre-sents a feature in an instance to be classi-

fied, and each branch represents a value

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that the node can assume. Instances are classified starting at the root node

and sorted based on their feature values. Figure is an example of a decision tree for

the training set of Table.

Using the decision tree depicted in Figure as an example, the instance ⟨at1 = a1, at2 =

b2, at3 = a3, at4 =b4⟩nodes: at1, at2, and finally at3, which would classify the instance as being posi-

tive (represented by the values ―Yes‖). The problem of constructing optimal binary

decision trees is an NPcomplete problem and thus theoreticians have searched for efficient heuristics for constructing

near-optimal decision trees.

Statistical Learning Algorithms:

Conversely to ANNs, statistical

approaches are characterized by having an explicit underlying probability model,

which provides a probability that an instance belongs in each class, rather than simply a classification. Linear discriminant

analysis (LDA) and the related Fisher's linear discriminant are simple methods

used in statistics and machine learning to find the linear combination of features

which best separate two or more classes of object (Friedman, 1989). LDA works when the measurements made on each ob-

servation are continuous quantities. When dealing with categorical variables, the

equivalent technique is Discriminant Correspondence Analysis (Mika et al.1999). Maximum entropy is another

general technique for estimating probabil-ity distributions from data. The overriding

principle in maximum entropy is that when nothing is known, the distribution should be as uniform as possible, that is, have

maximal entropy. Labeled training data is used to derive a set of constraints for the

model that characterize the class-specific expectations for the distribution. Csiszar (1996) provides a good tutorial introduc-

tion to maximum entropy techniques. Bayesian networks are the most well-

known representative of statistical learning algorithms. A comprehensive book on Bayesian networks is Jensen’s

(1996). Thus, in this study, apart from our brief description of Bayesian networks, we

mainly refer to more recent works.

Naive Bayes classifiers:

Naive Bayesian networks (NB) are

very simple Bayesian networks which are composed of directed acyclic graphs with only one parent (representing the unob-

served node) and several children (corre-sponding to observed nodes) with a strong

assumption of independence among child nodes in the context of their parent (Good, 1950).Thus, the independence model

(Naive Bayes) is based on estimating (Nilsson, 1965):

R= ( ) ( )

( ) ( ) ( ) ( )

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( ) ( ) ( ) ( )

| | | | | |

r r P i X P i P X i P i P X i

P j X P j P X j P j P X j = = ΠΠ

Comparing these two probabilities, the larger probability indicates that the class label value that is more likely to be

the actual label (if R>1: predict i predict j). Cestnik et al (1987) first used

the Naive Bayes in ML community. Since the Bayes classification algorithm uses a product operation to compute the probabil-

ities P(X, i), it is especially prone to being unduly impacted by probabilities of 0. This

can be avoided by using Laplace estimator or m-esimate, by adding one to all numera-tors and adding the number of added ones

to the denominator (Cestnik, 1990).

The assumption of independence among child nodes is clearly almost al-ways wrong and for this reason naive

Bayes classifiers are usually less accurate that other more sophisticated learning al-

gorithms (such ANNs). However, Domingos & Pazzani

(1997) performed a large-scale comparison of the naive Bayes classifier with state-of-

the-art algorithms for decision tree induc-tion, instance-based learning, and rule in-duction on standard benchmark datasets,

and found it to be sometimes superior to the other learning schemes, even on da-

tasets with substantial feature dependen-cies.

The basic independent Bayes mod-

el has been modified in various ways in attempts to improve its performance. At-

tempts to overcome the independence assumption are mainly based on adding extra edges to include some of the depend-

encies between the features, for example (Friedman et al. 1997). In this case, the

network has the limitation that each fea-ture can be related to only one other fea-

ture. Semi-naive Bayesian classifier is an-other important attempt to avoid the

independence assumption. (Kononenko, 1991), in which attributes are partitioned into groups and it is assumed that xi is

conditionally independent of xj if and only if they are in different groups.

The major advantage of the naive

Bayes classifier is its short computational

time for training. In addition, since the model has the form of a product, it can be

converted into a sum through the use of logarithms – with significant consequent computational advantages. If a feature is

numerical, the usual procedure is to discre-tize it during data pre-processing (Yang &

Webb, 2003), although a researcher can use the normal distribution to calculate probabilities (Bouckaert, 2004).

Bayesian Networks: A Bayesian Network (BN) is a

graphical model for probability relation-

ships among a set of variables (features). The Bayesian network structure S is a di-

rected acyclic graph (DAG) and the nodes in S are in one-to-one correspondence with the features X. The arcs represent casual

influences among the features while the lack of possible arcs in S encodes condi-

tional independencies. Moreover, a feature (node) is conditionally independent from its non-descendants given its parents (X1 is

conditionally independent from X2 given X3 if P(X1|X2,X3)=P(X1|X3) for all possi-

ble values of X1, X2, X3).

Speech recognition:

In Computer Science, Speech

recognition is the translation of spoken words into text. It is also known as

"automatic speech recognition", "ASR", "computer speech recognition", "speech to text", or just "STT".

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Speech Recognition is technology

that can translate spoken words into

text. Some SR systems use "training"

where an individual speaker reads sections

of text into the SR system. These systems

analyze the person's specific voice and use

it to fine tune the recognition of that

person's speech, resulting in more accurate

transcription. Systems that do not use

training are called "Speaker Independent"

systems. Systems that use training are

called "Speaker Dependent" systems.

Speech recognition applications

include voice user interfaces such as voice

dialing (e.g., "Call home"), call routing ("I

would like to make a collect

call"), demotic appliance control, search

(e.g., find a podcast where particular

words were spoken), simple data entry

(e.g., entering a credit card number),

preparation of structured documents (e.g.,

a radiology report), speech-to-text

processing (e.g., word

processors or emails), and aircraft (usually

termed Direct Voice Input).

The term voice recognition refers

to finding the identity of "who" is

speaking, rather than what they are

saying. Recognizing the speaker voice

recognition can simplify the task of

translating speech in systems that have

been trained on specific person's voices or

it can be used to authenticate or verify the

identity of a speaker as part of a security

process. "Voice recognition" means

"recognizing by voice", something humans

do all the time over the phone. As soon as

someone familiar says "hello" the listener

can identify them by the sound of their

voice alone.

Turing Test:

The Turing test is a test of

a machine's ability to exhibit intelligent

behavior. In Turing's original illustrative

example, a human judge engages in a natural language conversation with a human and a machine designed to

generate performance indistinguishable from that of a human being. All

participants are separated from one another. If the judge cannot reliably tell the

machine from the human, the machine is said to have passed the test. The test does not check the ability to give the correct

answer; it checks how closely the answer resembles typical human answers. The

conversation is limited to a text-only channel such as a computer keyboard and screen so that the result is

not dependent on the machine's ability to render words into audio.

The test was introduced by Alan

Turing in his 1950 paper Computing Machinery and Intelligence, which opens

with the words: "I propose to consider the question, 'Can machines think?'" Since "thinking" is difficult to define, Turing

chooses to "replace the question by another, which is closely related to it and

is expressed in relatively unambiguous words." Turing's new question is: "Are there imaginable digital computers which

would do well in the imitation game?" This question, Turing believed, is

one that can actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition

that "machines can think".

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ELIZA and PARRY

In 1966, Joseph

Weizenbaum created a program which

appeared to pass the Turing test. The

program, known as ELIZA, worked by

examining a user's typed comments for

keywords. If a keyword is found, a rule

that transforms the user's comments is

applied, and the resulting sentence is

returned. If a keyword is not found, ELIZA

responds either with a generic riposte or by

repeating one of the earlier comments. In

addition, Weizenbaum developed ELIZA

to replicate the behaviour of a Rogerian

psychotherapist, allowing ELIZA to be

"free to assume the pose of knowing

almost nothing of the real world." With

these techniques, Weizenbaum's program

was able to fool some people into

believing that they were talking to a real

person, with some subjects being "very

hard to convince that ELIZA

is nothuman." Thus, ELIZA is claimed by

some to be one of the programs (perhaps

the first) able to pass the Turing

Test, although this view is highly

contentious (see below).

Kenneth Colby created PARRY in

1972, a program described as "ELIZA with

attitude".[26] It attempted to model the

behaviour of a paranoidschizophrenic,

using a similar (if more advanced)

approach to that employed by

Weizenbaum. In order to validate the

work, PARRY was tested in the early

1970s using a variation of the Turing Test.

A group of experienced psychiatrists

analysed a combination of real patients

and computers running PARRY

through teleprinters. Another group of 33

psychiatrists were shown transcripts of the

conversations. The two groups were then

asked to identify which of the "patients"

were human and which were computer

programs. The psychiatrists were able to

make the correct identification only 48 per

cent of the time — a figure consistent with

random guessing.

In the 21st century, versions of

these programs (now known as

"chatterbots") continue to fool people.

"CyberLover", a malware program, preys

on Internet users by convincing them to

"reveal information about their identities

or to lead them to visit a web site that will

deliver malicious content to their

computers".The program has emerged as a

"Valentine-risk" flirting with people

"seeking relationships online in order to

collect their personal data".

The Chinese Room

Main article: Chinese room

John Searle's 1980 paper Minds,

Brains, and Programs proposed an

argument against the Turing Test known as

the "Chinese room" thought experiment.

Searle argued that software (such as

ELIZA) could pass the Turing Test simply

by manipulating symbols of which they

had no understanding. Without

understanding, they could not be described

as "thinking" in the same sense people do.

Therefore—Searle concludes—the Turing

Test cannot prove that a machine can

think. Searle's argument has been widely

criticized, but it has been endorsed as well.

Arguments such as that proposed

by Searle and others working on

the philosophy of mind sparked off a more

intense debate about the nature of

intelligence, the possibility of intelligent

machines and the value of the Turing test

that continued through the 1980s and

1990s.

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Siri (Speech Interpretation and Recognition Interface)

Siri (Speech Interpretation and Recognition Interface)

(pronounced /ˈsɪri/) is an intelligent personal assistant and knowledge

navigator which works as an application for Apple's iOS. The application uses a natural language user interface to answer

questions, make recommendations, and perform actions by delegating requests to a

set of web services. Apple claims that the software adapts to the user's individual preferences over time and personalizes

results, and performing tasks such as finding recommendations for nearby

restaurants, or getting directions.

Siri was originally introduced as an

iOS application available in the App

Store by Siri Inc. Siri Inc. was acquired by

Apple on April 28, 2010. Siri Inc. had

announced that their software would be

available for BlackBerry and for Android-

powered phones, but all development

efforts for non-Apple platforms were

cancelled after the acquisition by Apple.

Siri is now an integral part of iOS

5, and available only on the iPhone 4S,

launched on October 4, 2011. Despite this,

hackers were able to adapt Siri in prior

iPhones. On November 8, 2011, Apple

publicly announced that it had no plans to

support Siri on any of its older devices.

Siri Inc. was founded in 2007

by Dag Kittlaus (CEO), Adam Cheyer (VP

Engineering), andTom Gruber (CTO/VP

Design), together with Norman Winarsky

from SRI International's venture group. On

October 13, 2008, Siri announced it had

raised an $8.5 million Series A financing

round, led by Menlo

Ventures and Morgenthaler Ventures. In

November 2009, Siri raised a $15.5

million Series B financing round from the

same investors as in their previous round,

but led by Hong-Kong billionaire Li Ka-

shing. Dag Kittlaus left his position as

CEO of Siri at Apple after the launch of

the iPhone 4S.

Reception Of Siri:

Siri was met with a very positive

reaction for its ease of use and practicality,

as well as its apparent

"personality". Google’s executive

chairman and former chief, Eric Schmidt,

has conceded that Siri could pose a

"competitive threat" to the company’s core

search business. Google generates a large

portion of its revenue from clickable ad

links returned in the context of searches.

The threat comes from the fact that Siri is

a non-visual medium, therefore not

affording users with the opportunity to be

exposed to the clickable ad links. Writing

in The Guardian, journalist Charlie

Brooker described Siri's tone as "servile"

while also noting that it worked

"annoyingly well."

However, Siri was criticized by

organizations such as the American Civil

Liberties Union and NARAL Pro-Choice

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America after users found that it would not

provide information about the location of

birth control or abortion providers,

sometimes directing users to anti-

abortion crisis pregnancy centers instead.

Apple responded that this was a glitch

which would be fixed in the final version.

It was suggested that abortion providers

could not be found in a Siri search because

they did not use "abortion" in their

descriptions. At the time the controversy

arose, Siri would suggest locations to buy

illegal drugs, hire a prostitute, or dump a

corpse, but not find birth control or

abortion services. Apple responded that

this behavior is not intentional and will

improve as the product moves from beta to

final product.

Siri has not been well received by

some English speakers with distinctive

accents, including Scottish and Americans

from Boston or the South. Apple's Siri

FAQ states that, "as more people use Siri

and it’s exposed to more variations of a

language, its overall recognition of dialects

and accents will continue to improve, and

Siri will work even better."

Despite many functions still requiring the

use of the touchscreen, the National

Federation of the Blind describes the

iPhone as "the only fully

accessible handset that a blind person can

buy".

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Siri says some weird things

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Si

ri

s

ay

s

som

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we

ir

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ing

s

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LIZA

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41. http://www.theverge.com/201

1/10/12/2486618/siri-weird-iphone-4s

42. http://lifehacker.com/5846543/all-about-siri-your-iphones-new-

assistant

43. Supervised Machine Learning Survey Paper

44. Survey of Artificial

Intelligence for Prognostic 45. A SURVEY ON ARTIFICIAL

INTELLIGENCE BASED BRAIN PATHOLOGY

IDENTIFICATION TECHNIQUES IN

MAGNETIC RESONANCE IMAGES

46. http://dx.doi.org/10.1145%2F

365153.365168

47. http://en.wikipedia.org/wiki/Turing_test#cite_noteFOOTNOTEWeizenbaum196637-22

48. http://en.wikipedia.org/wiki/T

uring_test#cite_note-FOOTNOTEWeizenbaum196642-

23

49. http://en.wikipedia.org/wiki/Eric_Schmidt

50. http://www.norsys.com/tutoria

ls/netica/nt_toc_A.htm